Update app.py

app.py

@@ -13,7 +13,7 @@ import keras
 
 from underthesea import word_tokenize
 
-from phoBERT import BERT_predict
+#from phoBERT import BERT_predict
 
 #Load tokenizer
 # fp = Path(__file__).with_name('tokenizer.pkl')
@@ -21,12 +21,12 @@ from phoBERT import BERT_predict
 #     tokenizer = pickle.load(f)
 
 #Load LSTM
-# fp = Path(__file__).with_name('lstm_model.keras')
-LSTM_model = tf.keras.models.load_model('lstm_model.keras')
+fp = Path(__file__).with_name('lstm_model.keras')
+LSTM_model = tf.keras.models.load_model(fp)
 
 #Load GRU
-# fp = Path(__file__).with_name('gru_model.keras')
-GRU_model = tf.keras.models.load_model('gru_model.keras')
+fp = Path(__file__).with_name('gru_model.keras')
+GRU_model = tf.keras.models.load_model(fp)
 
 
 def tokenizer_pad(tokenizer,comment_text,max_length=200):
@@ -82,15 +82,15 @@ def judge(x):
 
   lstm_pred = LSTM_predict(x)
   gru_pred = GRU_predict(x)
-  bert_pred = BERT_predict(x)
+  # bert_pred = BERT_predict(x)
   #print(result)
   
   return_result = 'Result'
   result_lstm = np.round(lstm_pred, 2)
   result_gru = np.round(gru_pred, 2)
-  result_bert = np.round(bert_pred, 2)
+  # result_bert = np.round(bert_pred, 2)
   for i in range(6):
-    result.append((result_lstm[i]+result_gru[i]+result_bert[i])/3)
+    result.append((result_lstm[i]+result_gru[i])/2)
   
   return (result)